Identifying and implementing adaptive strategy in the U.S. military

Changes in the environment, including increased environmental complexity, require military supply units to employ a more adaptive strategy in order to enhance military agility. We extend the Lumpkin and Dess (1996) model and develop propositions that explore the interrelationships between/amongst entrepreneurial orientation (EO); opportunity recognition, evaluation and exploitation; environmental and organizational factors; and organizational performance. We propose that the innovativeness, proactiveness, and risk-taking dimensions of EO are of primary importance in identifying adaptive solutions and that these relationships are moderated by environmental factors. The autonomy and competitive aggressiveness dimensions of EO are important in implementing solutions as adaptive strategies, especially in a military context, and these relationships are moderated by organizational factors. This chapter extends existing theory developed primarily for the civilian sector to the military. Military organizations are more rigid hierarchical structures, and have different measures of performance. At an applied level, this research provides insights for military commanders that can potentially enhance agility and adaptability.

Adaptive GPS duty cycling and radio ranging for energy-efficient localization

This paper addresses the tradeoff between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty in order to prolong node lifetime. We use empirical GPS and radio contact data from a largescale animal tracking deployment to model node mobility, GPS and radio performance. These models are used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose a versatile contact logging strategy that relies on RSSI ranging and GPS lock back-offs for reducing the node energy consumption relative to GPS duty cycling. Results show that our strategy can cut the node energy consumption by half while meeting application specific positioning criteria.

Adaptive wing/aerofoil design optimisation using MOEA coupled to uncertainty design method

The use of adaptive wing/aerofoil designs is being considered as promising techniques in aeronautic/aerospace since they can reduce aircraft emissions, improve aerodynamic performance of manned or unmanned aircraft. The paper investigates the robust design and optimisation for one type of adaptive techniques; Active Flow Control (AFC) bump at transonic flow conditions on a Natural Laminar Flow (NLF) aerofoil designed to increase aerodynamic efficiency (especially high lift to drag ratio). The concept of using Shock Control Bump (SCB) is to control supersonic flow on the suction/pressure side of NLF aerofoil: RAE 5243 that leads to delaying shock occurrence or weakening its strength. Such AFC technique reduces total drag at transonic speeds due to reduction of wave drag. The location of Boundary Layer Transition (BLT) can influence the position the supersonic shock occurrence. The BLT position is an uncertainty in aerodynamic design due to the many factors, such as surface contamination or surface erosion. The paper studies the SCB shape design optimisation using robust Evolutionary Algorithms (EAs) with uncertainty in BLT positions. The optimisation method is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. Two test cases are conducted; the first test assumes the BLT is at 45% of chord from the leading edge and the second test considers robust design optimisation for SCB at the variability of BLT positions and lift coefficient. Numerical result shows that the optimisation method coupled to uncertainty design techniques produces Pareto optimal SCB shapes which have low sensitivity and high aerodynamic performance while having significant total drag reduction.

Adaptive cache aware multiprocessor scheduling framework

Computer resource allocation represents a significant challenge particularly for multiprocessor systems, which consist of shared computing resources to be allocated among co-runner processes and threads. While an efficient resource allocation would result in a highly efficient and stable overall multiprocessor system and individual thread performance, ineffective poor resource allocation causes significant performance bottlenecks even for the system with high computing resources. This thesis proposes a cache aware adaptive closed loop scheduling framework as an efficient resource allocation strategy for the highly dynamic resource management problem, which requires instant estimation of highly uncertain and unpredictable resource patterns. Many different approaches to this highly dynamic resource allocation problem have been developed but neither the dynamic nature nor the time-varying and uncertain characteristics of the resource allocation problem is well considered. These approaches facilitate either static and dynamic optimization methods or advanced scheduling algorithms such as the Proportional Fair (PFair) scheduling algorithm. Some of these approaches, which consider the dynamic nature of multiprocessor systems, apply only a basic closed loop system; hence, they fail to take the time-varying and uncertainty of the system into account. Therefore, further research into the multiprocessor resource allocation is required. Our closed loop cache aware adaptive scheduling framework takes the resource availability and the resource usage patterns into account by measuring time-varying factors such as cache miss counts, stalls and instruction counts. More specifically, the cache usage pattern of the thread is identified using QR recursive least square algorithm (RLS) and cache miss count time series statistics. For the identified cache resource dynamics, our closed loop cache aware adaptive scheduling framework enforces instruction fairness for the threads. Fairness in the context of our research project is defined as a resource allocation equity, which reduces corunner thread dependence in a shared resource environment. In this way, instruction count degradation due to shared cache resource conflicts is overcome. In this respect, our closed loop cache aware adaptive scheduling framework contributes to the research field in two major and three minor aspects. The two major contributions lead to the cache aware scheduling system. The first major contribution is the development of the execution fairness algorithm, which degrades the co-runner cache impact on the thread performance. The second contribution is the development of relevant mathematical models, such as thread execution pattern and cache access pattern models, which in fact formulate the execution fairness algorithm in terms of mathematical quantities. Following the development of the cache aware scheduling system, our adaptive self-tuning control framework is constructed to add an adaptive closed loop aspect to the cache aware scheduling system. This control framework in fact consists of two main components: the parameter estimator, and the controller design module. The first minor contribution is the development of the parameter estimators; the QR Recursive Least Square(RLS) algorithm is applied into our closed loop cache aware adaptive scheduling framework to estimate highly uncertain and time-varying cache resource patterns of threads. The second minor contribution is the designing of a controller design module; the algebraic controller design algorithm, Pole Placement, is utilized to design the relevant controller, which is able to provide desired timevarying control action. The adaptive self-tuning control framework and cache aware scheduling system in fact constitute our final framework, closed loop cache aware adaptive scheduling framework. The third minor contribution is to validate this cache aware adaptive closed loop scheduling framework efficiency in overwhelming the co-runner cache dependency. The timeseries statistical counters are developed for M-Sim Multi-Core Simulator; and the theoretical findings and mathematical formulations are applied as MATLAB m-file software codes. In this way, the overall framework is tested and experiment outcomes are analyzed. According to our experiment outcomes, it is concluded that our closed loop cache aware adaptive scheduling framework successfully drives co-runner cache dependent thread instruction count to co-runner independent instruction count with an error margin up to 25% in case cache is highly utilized. In addition, thread cache access pattern is also estimated with 75% accuracy.

Adaptive radiotherapy for virally mediated head and neck cancer : predicting which patients may benefit most

Background: Recent clinical studies have demonstrated an emerging subgroup of head and neck cancers that are virally mediated. This disease appears to be a distinct clinical entity with patients presenting younger and with more advanced nodal disease, having lower tobacco and alcohol exposure and highly radiosensitive tumours. This means they are living longer, often with the debilitating functional side effects of treatment. The primary objective of this study was to determine how virally mediated nasopharyngeal and oropharyngeal cancers respond to radiation therapy treatment. The aim was to determine risk categories and corresponding adaptive treatment management strategies to proactively manage these patients. Method/Results: 121 patients with virally mediated, node positive nasopharyngeal or oropharyngeal cancer who received radiotherapy treatment with curative intent between 2005 and 2010 were studied. Relevant patient demographics including age, gender, diagnosis, TNM stage, pre-treatment nodal size and dose delivered was recorded. Each patient’s treatment plan was reviewed to determine if another computed tomography (re-CT) scan was performed and at what time point (dose/fraction) this occurred. The justification for this re-CT was determined using four categories: tumour and/or nodal regression, weight loss, both or other. Patients who underwent a re-CT were further investigated to determine whether a new plan was calculated. If a re-plan was performed, the dosimetric effect was quantified by comparing dose volume histograms of planning target volumes and critical structures from the actual treatment delivered and the original treatment plan. Preliminary results demonstrated that 25/121 (20.7%) patients required a re-CT and that these re-CTs were performed between fractions 20 to 25 of treatment. The justification for these re-CTs consisted of a combination of tumour and/or nodal regression and weight loss. 16/25 (13.2%) patients had a replan calculated. 9 (7.4%) of these replans were implemented clinically due to the resultant dosimetric effect calculated. The data collected from this assessment was statistically analysed to identify the major determining factors for patients to undergo a re-CT and/or replan. Specific factors identified included nodal size and timing of the required intervention (i.e. how when a plan is to be adapted). This data was used to generate specific risk profiles that will form the basis of a biologically guided adaptive treatment management strategy for virally mediated head and neck cancer. Conclusion: Preliminary data indicates that virally mediated head and neck cancers respond significantly during radiation treatment (tumour and/or nodal regression and weight loss). Implications of this response are the potential underdosing or overdosing of tumour and/or surrounding critical structures. This could lead to sub-optimal patient outcomes and compromised quality of life. Consequently, the development of adaptive treatment strategies that improve organ sparing for this patient group is important to ensure delivery of the prescribed dose to the tumour volume whilst minimizing the dose received to surrounding critical structures. This could reduce side effects and improve overall patient quality of life. The risk profiles and associated adaptive treatment approaches developed in this study will be tested prospectively in the clinical setting in Phase 2 of this investigation.

Adaptive stepsize based on control theory for stochastic differential equations

The numerical solution of stochastic differential equations (SDEs) has been focused recently on the development of numerical methods with good stability and order properties. These numerical implementations have been made with fixed stepsize, but there are many situations when a fixed stepsize is not appropriate. In the numerical solution of ordinary differential equations, much work has been carried out on developing robust implementation techniques using variable stepsize. It has been necessary, in the deterministic case, to consider the "best" choice for an initial stepsize, as well as developing effective strategies for stepsize control-the same, of course, must be carried out in the stochastic case. In this paper, proportional integral (PI) control is applied to a variable stepsize implementation of an embedded pair of stochastic Runge-Kutta methods used to obtain numerical solutions of nonstiff SDEs. For stiff SDEs, the embedded pair of the balanced Milstein and balanced implicit method is implemented in variable stepsize mode using a predictive controller for the stepsize change. The extension of these stepsize controllers from a digital filter theory point of view via PI with derivative (PID) control will also be implemented. The implementations show the improvement in efficiency that can be attained when using these control theory approaches compared with the regular stepsize change strategy.

Optimized strategy for integrated traffic and transit signal control

This paper proposes a unique and innovative approach to integrate transit signal priority control into a traffic adaptive signal control strategy. The proposed strategy was named OSTRAC (Optimized Strategy for integrated TRAffic and TRAnsit signal Control). The cornerstones of OSTRAC include an online microscopic traffic f low prediction model and a Genetic Algorithm (GA) based traffic signal timing module. A sensitivity analysis was conducted to determine the critical GA parameters. The developed traffic f low model demonstrated reliable prediction results through a test. OSTRAC was evaluated by comparing its performance to three other signal control strategies. The evaluation results revealed that OSTRAC efficiently and effectively reduced delay time of general traffic and also transit vehicles.

Developing an adaptive radiotherapy technique for virally mediated head and neck cancer

Purpose Virally mediated head and neck cancers (VMHNC) often present with nodal involvement and are highly radioresponsive, meaning that treatment plan adaptation during radiotherapy (RT) in a subset of patients is required. We sought to determine potential risk profiles and a corresponding adaptive treatment strategy for these patients. Methodology 121 patients with virally mediated, node positive nasopharyngeal (Epstein Barr Virus positive) or oropharyngeal (Human Papillomavirus positive) cancers, receiving curative intent RT were reviewed. The type, frequency and timing of adaptive interventions, including source-to-skin distance (SSD) corrections, re-scanning and re-planning, were evaluated. Patients were reviewed based on the maximum size of the dominant node to assess the need for plan adaptation. Results Forty-six patients (38%) required plan adaptation during treatment. The median fraction at which the adaptive intervention occurred was 26 for SSD corrections and 22 for re-planning CTs. A trend toward 3 risk profile groupings was discovered: 1) Low risk with minimal need (< 10%) for adaptive intervention (dominant pre-treatment nodal size of ≤ 35 mm), 2) Intermediate risk with possible need (< 20%) for adaptive intervention (dominant pre-treatment nodal size of 36 mm – 45 mm) and 3) High-risk with increased likelihood (> 50%) for adaptive intervention (dominant pre-treatment nodal size of ≥ 46 mm). Conclusion In this study, patients with VMHNC and a maximum dominant nodal size of > 46 mm were identified at a higher risk of requiring re-planning during a course of definitive RT. Findings will be tested in a future prospective adaptive RT study.

Adaptive finite element analysis of elliptic problems based on bubble-type local mesh generation

A new mesh adaptivity algorithm that combines a posteriori error estimation with bubble-type local mesh generation (BLMG) strategy for elliptic differential equations is proposed. The size function used in the BLMG is defined on each vertex during the adaptive process based on the obtained error estimator. In order to avoid the excessive coarsening and refining in each iterative step, two factor thresholds are introduced in the size function. The advantages of the BLMG-based adaptive finite element method, compared with other known methods, are given as follows: the refining and coarsening are obtained fluently in the same framework; the local a posteriori error estimation is easy to implement through the adjacency list of the BLMG method; at all levels of refinement, the updated triangles remain very well shaped, even if the mesh size at any particular refinement level varies by several orders of magnitude. Several numerical examples with singularities for the elliptic problems, where the explicit error estimators are used, verify the efficiency of the algorithm. The analysis for the parameters introduced in the size function shows that the algorithm has good flexibility.

A second order control-volume finite-element least-squares strategy for simulating diffusion in strongly anisotropic media

An unstructured mesh �nite volume discretisation method for simulating di�usion in anisotropic media in two-dimensional space is discussed. This technique is considered as an extension of the fully implicit hybrid control-volume �nite-element method and it retains the local continuity of the ux at the control volume faces. A least squares function recon- struction technique together with a new ux decomposition strategy is used to obtain an accurate ux approximation at the control volume face, ensuring that the overall accuracy of the spatial discretisation maintains second order. This paper highlights that the new technique coincides with the traditional shape function technique when the correction term is neglected and that it signi�cantly increases the accuracy of the previous linear scheme on coarse meshes when applied to media that exhibit very strong to extreme anisotropy ratios. It is concluded that the method can be used on both regular and irregular meshes, and appears independent of the mesh quality.